Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Authors: Jiazhi Xu, Sheng Huang, Fengtao Zhou, Luwen Huangfu, Daniel Zeng, Bo Liu
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results on MS-COCO and NUS-WIDE datasets demonstrate that our framework can be easily plugged into many MLIC approaches and improve performances of recent stateof-the-art approaches. |
| Researcher Affiliation | Collaboration | 1School of Big Data and Software Engineering, Chongqing University 2Fowler College of Business & CHDMA, San Diego State University, 3Institute of Automation, Chinese Academy of Sciences, 4JD Finance America Corporation |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is released at https://github.com/Robbie-Xu/CPSD. |
| Open Datasets | Yes | Two widely used MLIC datasets, named MS-COCO and NUS-WIDE, are used for the evaluation of our method. MS-COCO contains 122,218 images with 80 categories of objects in natural scenes, including 82,081 images for training and 40,137 images for validation. In the official partition of NUS-WIDE dataset, it contains 125,449 labeled training pictures and 83,898 labeled test pictures from Flickr, which share 81 labels in total. |
| Dataset Splits | Yes | MS-COCO contains 122,218 images with 80 categories of objects in natural scenes, including 82,081 images for training and 40,137 images for validation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'ASL' and model types like 'ResNet101-TF' and 'GloVe', but it does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | All experiments follow a training pipeline where Adam optimizer is used with weight decay of 10-4 under a batch size of 32. ASL is applied as the default classification loss function, and the hyper-parameters of ASL are simply left as their default settings. τ in Equation 1 is set to be 3. We set the training epoch to be 20 and 80 for sub-models and the compact global model individually. |